Method and systems for anatomy/view classification in x-ray imaging

ABSTRACT

Various methods and systems are provided for x-ray imaging. In one embodiment, a method for an image pasting examination comprises acquiring, via an optical camera and/or depth camera, image data of a subject, controlling an x-ray source and an x-ray detector according to the image data to acquire a plurality of x-ray images of the subject, and stitching the plurality of x-ray images into a single x-ray image. In this way, optimal exposure techniques may be used for individual acquisitions in an image pasting examination such that the optimal dose is utilized, stitching quality is improved, and registration failures are avoided.

FIELD

Embodiments of the subject matter disclosed herein relate to x-rayimaging.

BACKGROUND

Imaging technologies such as x-ray imaging allow for non-invasiveacquisition of images of internal structures or features of a subject,such as a patient. Digital x-ray imaging systems produce digital datawhich can be processed into radiographic images. In digital x-rayimaging systems, radiation from a source is directed toward the subject.A portion of the radiation passes through the subject and impacts adetector. The detector includes an array of discrete picture elements ordetector pixels and generates output signals based upon the quantity orintensity of the radiation impacting each pixel region. The outputsignals are subsequently processed to generate an image that may bedisplayed for review. These images are used to identify and/or examinethe internal structures and organs within a patient's body.

BRIEF DESCRIPTION

In one embodiment, a method comprises controlling an x-ray source and anx-ray detector to acquire an image of a subject, classifying, with atrained neural network, an anatomy/view depicted in the image,performing post-processing of the image based on the anatomy/view, anddisplaying the post-processed image. In this way, the user workflow forx-ray image acquisition and processing may be simplified, and theconsistency in appearance of radiographic images as well as theregistration accuracy for image pasting examinations is improved.

It should be understood that the brief description above is provided tointroduce in simplified form a selection of concepts that are furtherdescribed in the detailed description. It is not meant to identify keyor essential features of the claimed subject matter, the scope of whichis defined uniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be better understood from reading thefollowing description of non-limiting embodiments, with reference to theattached drawings, wherein below:

FIG. 1 shows an example x-ray imaging system according to an embodiment;

FIG. 2 shows a high-level flow chart illustrating an example method forautomatic anatomy/view classification according to an embodiment;

FIG. 3 shows a high-level flow chart illustrating an example method forpost-processing of an acquired radiographic image based on ananatomy/view classification according to an embodiment;

FIG. 4 shows a set of images illustrating the method of FIG. 3 accordingto an embodiment;

FIG. 5 shows a high-level flow chart illustrating an example method forimage pasting of radiographic images based on anatomy/viewclassifications according to an embodiment;

FIG. 6 shows a high-level flow chart illustrating an example method fordetermining anatomy/view classifications based on predictions ofanatomy/view classifications for multiple radiographic images accordingto an embodiment;

FIG. 7 shows a set of images illustrating the method of FIG. 5 accordingto an embodiment;

FIG. 8 shows a high-level flow chart illustrating an example method foranatomy/view classification for acquisition protocol selection accordingto an embodiment; and

FIG. 9 shows a set of images illustrating anatomy/view classificationbased on camera data according to an embodiment.

DETAILED DESCRIPTION

The following description relates to various embodiments of x-rayimaging. In particular, systems and methods are provided for automaticanatomy/view classification for x-ray imaging. An x-ray imaging system,such as the x-ray imaging system depicted in FIG. 1, includes a trainedclassification network for automatically determining the anatomy/view ofan imaging subject. An anatomy/view comprises an orientation andpositioning of the imaging subject relative to the x-ray source andx-ray detector. A method for x-ray imaging, such as the method depictedin FIG. 2, includes classifying the anatomy/view depicted in one or moreimages and post-processing the one or more images based on theanatomy/view. For radiographic image processing, adaptive parametersrelating to the overall appearance, such as parameters relating to edgedetails, contrast, noise level, and so on, are needed for differentanatomy/views. A method for radiographic image processing, such as themethod depicted in FIGS. 3 and 4, includes automatically adjusting suchparameters based on the anatomy/view classified by the classificationnetwork. In this way, images are processed with the correct protocol,even if a user of the imaging system selects an incorrect protocol viathe imaging system user interface. As the user does not need to selectan optimal view protocol for displaying the image, the workflowefficiency is increased while the consistency of displayed images isimproved.

Further, image pasting, or the creation of a composite image, is usuallyaccomplished by having a system for acquiring images with a totalfield-of-view larger than the detector field-of-view (FOV). Forapplications such as full-spine imaging or long-legs imaging, the totalcoverage of anatomy (e.g., 60-120 cm) exceeds that of most currentdetectors and film-screen systems. In one approach to overcome thelimitations of the detector FOV, multiple images are acquired during animage pasting examination with overlapping FOVs and stitched together.Historically, images were acquired with the detector FOV, and thevarious images are then cut manually by a radiologist to avoid overlapsand repasted manually to reconstruct an image with the total FOV. Morerecently, automatic techniques for digitally pasting successive imageshave increased the accuracy of image pasting examinations. Often,multiple orientations or views of a subject or patient are acquiredduring image pasting examinations. In typical workflows for acquiringmultiple views of the subject during image pasting examinations, theuser selects different protocols for each view. If an incorrect viewprotocol is selected, the images may be mis-registered for a view,thereby reducing the image pasting accuracy. A method for improving theworkflow efficiency and improving the success of image pastingexaminations, such as the method depicted in FIGS. 5-7, includes usingthe classification network to identify the anatomy/view in each image,evaluating the classifications of anatomy/view to ensure that theclassifications are correct, and then performing image pasting withappropriate view protocols automatically selected based on theclassifications.

Further, in some examples, a camera may be provided in an x-ray imagingsystem to acquire photographic images in the optical domain. A methodfor x-ray imaging, such as the method depicted in FIGS. 8 and 9,includes automatically classifying an anatomy/view in camera dataacquired with the camera, and automatically selecting an acquisitionprotocol (e.g., selecting x-ray tube current and/or x-ray tube voltage)based on the anatomy/view of the subject. In this way, appropriateacquisition protocols as well as view protocols may be determinedwithout user input, thereby improving workflow efficiency, increasingthe accuracy and consistency of radiographic imaging, and enablingreduced dosage.

Turning now to FIG. 1, a block diagram of an x-ray imaging system 100 inaccordance with an embodiment is shown. The x-ray imaging system 100includes an x-ray source 111 which radiates x-rays, a stand 132 uponwhich the subject 105 stands during an examination, and an x-raydetector 134 for detecting x-rays radiated by the x-ray source 111 andattenuated by the subject 105. The x-ray detector 134 may comprise, asnon-limiting examples, a scintillator, one or more ion chamber(s), alight detector array, an x-ray exposure monitor, an electric substrate,and so on. The x-ray detector 134 is mounted on a stand 138 and isconfigured so as to be vertically moveable according to an imaged regionof the subject.

The operation console 160 comprises a processor 161, a memory 162, auser interface 163, a motor drive 145 for controlling one or more motors143, an x-ray power unit 114, an x-ray controller 116, a camera dataacquisition unit 121, an x-ray data acquisition unit 135, and an imageprocessor 150. X-ray image data transmitted from the x-ray detector 134is received by the x-ray data acquisition unit 135. The collected x-rayimage data are image-processed by the image processor 150. A displaydevice 155 communicatively coupled to the operating console 160 displaysan image-processed x-ray image thereon.

The x-ray source 111 is supported by a support post 141 which may bemounted to a ceiling (e.g., as depicted) or mounted on a moveable standfor positioning within an imaging room. The x-ray source 111 isvertically moveable relative to the subject or patient 105. For example,one of the one or more motors 143 may be integrated into the supportpost 141 and may be configured to adjust a vertical position of thex-ray source 111 by increasing or decreasing the distance of the x-raysource 111 from the ceiling or floor, for example. To that end, themotor drive 145 of the operation console 160 may be communicativelycoupled to the one or more motors 143 and configured to control the oneor more motors 143. The one or more motors 143 may further be configuredto adjust an angular position of the x-ray source 111 to change afield-of-view of the x-ray source 111, as described further herein.

The x-ray power unit 114 and the x-ray controller 116 supply power of asuitable voltage current to the x-ray source 111. A collimator (notshown) may be fixed to the x-ray source 111 for designating anirradiated field-of-view of an x-ray beam. The x-ray beam radiated fromthe x-ray source 111 is applied onto the subject via the collimator.

The x-ray source 111 and the camera 120 may pivot or rotate relative tothe support post 141 in an angular direction 129 to image differentportions of the subject 105. For example, during an image pastingexamination, multiple x-ray images of the subject 105 may be acquired atdifferent angles and pasted or stitched together to form a single image.As depicted, the x-ray source 111 may be oriented with a firstfield-of-view 126 to acquire a first x-ray image. The x-ray source 111may then be rotated in the angular direction 129 to a secondfield-of-view 127 to acquire a second x-ray image. The x-ray source 111may then be rotated in the angular direction 129 to a thirdfield-of-view 128 to acquire a third x-ray image. The threefields-of-view 126, 127, and 128 are depicted as partially overlapping.The three x-ray images acquired may thus be stitched together asdescribed further herein to form a single x-ray image.

Thus, the x-ray imaging system 100 may be used to perform an imagestitching examination as described hereinabove, in addition toperforming typical single-energy x-ray image acquisitions.

Such methods, as described further herein, include controlling theprocessor 161 and/or the image processor 150 to provide an image to aclassification network 170 for automatically identifying an anatomy/viewdepicted in the image. The classification network 170 may comprise aneural network model, such as a convolutional neural network, trained ona set of radiographic images with corresponding labels of theanatomy/view depicted in the images. For example, the classificationnetwork 170 may comprise an efficient convolutional neural network suchas a MobileNet model. The classification network 170 may be initiallytrained or primed with a training dataset of large-scale non-radiologyimages, for example by loading pre-trained parameters obtained bytraining the neural network with the training dataset of large-scalenon-radiology images.

Memory 162 stores executable instructions 172 that when executed causeone or more of the processor 161 and the image processor 150 to performone or more actions. Example methods that may be stored as theexecutable instructions 172 are described further herein with regard toFIGS. 2, 3, 5, 6, and 8.

In some examples, the x-ray imaging system 100 may include a camera 120positioned adjacent to the x-ray source 111 and co-calibrated orco-aligned with the x-ray source 111. The camera 120 may comprise anoptical camera that detects electromagnetic radiation in the opticalrange. Additionally or alternatively, the camera 120 may comprise adepth camera or range imaging camera. As an illustrative andnon-limiting example, the camera 120 configured as a depth camera mayinclude an optical camera, an infrared camera, and an infrared projectorwhich projects infrared dots in the field-of-view of the camera 120. Theinfrared camera images the dots, which in turn may be used to measuredepth within the optical camera of the camera 120. As anotherillustrative and non-limiting example, the camera 120 may comprise atime-of-flight camera. The camera 120 is communicatively coupled to thecamera data acquisition unit 121 of the operation console 160. Cameradata acquired or generated by the camera 120 may thus be transmitted tothe camera data acquisition unit 121, which in turn provides acquiredcamera image data to the image processor 150 for image processing. Thefield-of-view of the camera 120 may overlap the field-of-view of thex-ray source 111. In this way, the field-of-view of the x-ray source 111may be determined automatically from camera data recorded by the camera120. Further, as described further herein with regard to FIGS. 8 and 9,the image processor 150 may process acquired camera data with theclassification network 170 to automatically determine the anatomy/viewof the subject 105 with respect to the x-ray source 111 and the x-raydetector 134. The image processor 150 may then determine acquisitionparameters for controlling the x-ray source 111 and the x-ray detector134 to acquire one or more radiographic images, as well as viewparameters for processing the acquired image(s), based on theanatomy/view determined in the camera data.

FIG. 2 shows a high-level flow chart illustrating an example method 200for automatic anatomy/view classification according to an embodiment. Inparticular, method 200 relates to automatically classifying theanatomy/view depicted in one or more images with a classificationnetwork, such as the classification network 170. Method 200 is describedwith regard to the systems and components of FIG. 1, though it should beappreciated that the method 200 may be implemented with other systemsand components without departing from the scope of the presentdisclosure. Method 200 may be stored as executable instructions 172 inthe non-transitory memory 162 of the operation console 160, for example,and may be executed by one or more of the processor 161 and the imageprocessor 150 to perform the actions described herein below.

Method 200 begins at 205. At 205, method 200 acquires one or moreimages. In some examples, method 200 may control the x-ray source 111 togenerate a beam of x-rays towards the x-ray detector 134, and convertsthe x-rays detected by the x-ray detector 134 to a radiographic image.In other examples, method 200 controls the x-ray source 111 and thex-ray detector 134 to acquire a series of radiographic images, whileadjusting the relative position of the x-ray source 111 and the x-raydetector 134 such that the radiographic images depict different butadjacent regions of the subject 105. In such examples, method 200 maycontrol the x-ray source 111 and the x-ray detector 134 to acquiremultiple images of the subject 105 in different views. For example,method 200 may acquire two or more of an image of the subject 105 with aright lateral view, an image of the subject 105 with ananterior-posterior (AP) view, an image of the subject 105 with aposterior-anterior (PA) view, and an image of the subject 105 with aleft lateral view. As another example, method 200 may acquire multipleimages in multiple views of the subject 105, wherein the multiple imagesdepict different but adjacent regions of the subject 105.

After acquiring the image(s) at 205, method 200 continues to 210. At210, method 200 inputs the acquired image(s) to a classificationnetwork, such as the classification network 170. The classificationnetwork 170 is trained to classify an anatomy/view depicted in theacquired image(s). For example, the anatomy may comprise a head, spine,abdomen, leg, and so on, while the view may indicate an orientation ofthe subject with respect to the x-ray detector, expressed in anatomicalterms, such as lateral, anterior-posterior (AP), posterior-anterior(PA), and so on. The anatomy/view classified by the classificationnetwork may thus be output as a combination of the anatomy depicted inthe image as well as the view of the anatomy depicted in the image. Forexample, the output of the classification network 170 may comprise ananatomy/view classification such as Spine APPA, Spine Right Lateral,Spine Left Lateral, Leg APPA, and so on.

Continuing at 215, method 200 receives a classification of theanatomy/view depicted in the one or more acquired image(s) from theclassification network 170. That is, for each acquired image input tothe classification network 170, method 200 receives a correspondinganatomy/view classification. For example, if a single radiographic imageis acquired at 205, method 200 receives a classification of theanatomy/view depicted in the single radiographic image. If a series ofradiographic images are acquired at 205 for image pasting, then method200 receives a classification of the anatomy/view depicted in eachradiographic image in the series.

At 220, method 200 performs post-processing on the one or more acquiredimage(s) based on the classification of the anatomy/view. For example,based on the classification of the anatomy/view in an image, method 200selects post-processing parameters according to the classification forpost-processing the image, as different post-processing preferencesregarding edge details, contrast, noise level, and so on may be used fordifferent anatomy/views. Therefore, a post-processing protocolcomprising a set of post-processing parameters may be configured foreach anatomy/view, and method 200 may select the appropriatepost-processing protocol based on the classification. Method 200 thenprocesses the acquired image according to the post-processing protocolor according to the post-processing parameters selected based on theanatomy/view classification. In this way, the automatic classificationof the anatomy/view enables the automatic selection of the correctparameters for post-processing of images, thereby ensuring a consistentappearance for the user.

As another example, for multiple images acquired in a view, thepost-processing of the acquired images may include post-processing ofthe contrast, edge details, noise level, and so on as mentionedhereinabove for a single image, and may further include image pasting orimage stitching. That is, in some examples, the post-processing of theacquired images may include stitching the multiple images together intoa single image with a larger field-of-view than the field-of-view ofeach individual image. In order to ensure that the correct images arestitched in accordance with the correct protocol, method 200automatically selects the protocol for stitching the images based on theanatomy/view classification for each image. In this way, the imagepasting registration success rate is improved while eliminating the needfor the user to manually select the protocol for each view.

At 225, method 200 outputs the post-processed image(s). Method 200 mayoutput the post-processed image(s), for example, to the display device155 for display to the user. In addition, method 200 may output thepost-processed image(s) to the memory 162 for later review, and/or to apicture archiving and communication system (PACS). After outputting thepost-processed image(s), method 200 then returns.

FIG. 3 shows a high-level flow chart illustrating an example method 300for post-processing of an acquired radiographic image based on ananatomy/view classification according to an embodiment. In particular,method 300 relates to automatically selecting parameters forpost-processing of an acquired image based on an automaticallyclassified anatomy/view depicted in the acquired image. Method 300 isdescribed with regard to the systems and components of FIG. 1, though itshould be appreciated that the method 300 may be implemented with othersystems and components without departing from the scope of the presentdisclosure. Method 300 may be stored as executable instructions 172 inthe non-transitory memory 162 of the operation console 160, for example,and may be executed by one or more of the processor 161 and the imageprocessor 150 to perform the actions described herein below.

Method 300 begins at 305. At 305, method 300 receives a selection of aprotocol. Method 300 specifically receives a selection of an acquisitionprotocol specifying one or more parameters for acquiring an image, suchas tube voltage, tube current, and so on. Method 300 may receive theselection of the protocol, for example, via the user interface 163.Continuing at 310, method 300 controls an x-ray source, such as thex-ray source 111, according to the selected protocol to acquire animage. Method 300 may further control an x-ray detector, such as thex-ray detector 134, to acquire the image in accordance with theprotocol.

After acquiring the image, method 300 continues to 315. At 315, method300 inputs the acquired image to a classification network, such as theclassification network 170. As discussed hereinabove, the classificationnetwork 170 is trained to classify an anatomy/view depicted in theacquired image. For example, the anatomy may comprise a head, spine,abdomen, leg, and so on, while the view may indicate an orientation ofthe subject with respect to the x-ray detector, expressed in anatomicalterms, such as lateral, anterior-posterior (AP), posterior-anterior(PA), and so on. The anatomy/view classified by the classificationnetwork may thus be output as a combination of the anatomy depicted inthe image as well as the view of the anatomy depicted in the image. Forexample, the output of the classification network 170 may comprise ananatomy/view classification such as Spine APPA, Spine Right Lateral,Spine Left Lateral, Leg APPA, and so on.

Thus, continuing at 320, method 300 receives a classification of theanatomy/view depicted in the acquired image from the classificationnetwork 170. At 325, method 300 performs post-processing of the acquiredimage based on the anatomy/view classification. For example, based onthe classification of the anatomy/view in an image, method 300 selectspost-processing parameters according to the classification forpost-processing the image, as different post-processing preferencesregarding edge details, contrast, noise level, and so on may be used fordifferent anatomy/views. Therefore, a post-processing protocolcomprising a set of post-processing parameters may be configured foreach anatomy/view, and method 300 may select the appropriatepost-processing protocol based on the classification. Method 300 thenprocesses the acquired image according to the post-processing protocol,or according to the post-processing parameters selected based on theanatomy/view classification. In this way, the automatic classificationof the anatomy/view enables the automatic selection of the correctparameters for post-processing of images, thereby ensuring a consistentappearance for the user.

At 330, method 300 outputs the post-processed image. For example, method300 may output the post-processed image to the display device 155 fordisplay to the user. Additionally, method 300 may output thepost-processed image to the memory 162 for storage, and/or a PACS forremote storage and/or review. Method 300 then returns.

As an illustrative example, FIG. 4 shows a set of images 400illustrating the automatic classification of an acquired image. The setof images 400 includes an example acquired image 405. The set of images400 further includes an example output 410 of the classification networkincluding the acquired image 405 and the anatomy/view classification 412for the acquired image 405. As depicted, the anatomy/view classification412 indicates that the view of the acquired image 405 is a “LeftLateral” view. In some examples, the anatomy/view classification 412 maybe superimposed on the acquired image 405 and displayed to the user, forexample via the display device 155, for confirmation. In other examples,the anatomy/view classification 412 may not be superimposed on theacquired image 405 or displayed to the user. Further, while theanatomy/view classification 412 as depicted does not explicitly indicatethe anatomy, in some examples the anatomy/view classification 412 mayfurther explicitly indicate the anatomy, for example by indicating the“Neck” anatomy in addition to the “Left Lateral” view in theanatomy/view classification 412. As discussed hereinabove, the acquiredimage 405 is post-processed according to the anatomy/view classification412. In particular, one or more post-processing parameters including butnot limited to the field-of-view, contrast, edge details, noisereduction, and so on may be determined based on the anatomy/viewclassification 412, and the acquired image 405 may be processedaccording to the one or more post-processing parameters to generate thepost-processed image 415. In this way, the workload of manuallyselecting a post-processing or view protocol may be relieved whilemaintaining a high-quality post-processing performance.

FIG. 5 shows a high-level flow chart illustrating an example method 500for image pasting of radiographic images based on anatomy/viewclassifications according to an embodiment. In particular, method 500relates to acquiring multiple images in at least one view andautomatically post-processing the multiple images based on an automaticanatomy/view classification to obtain at least one stitched image.Method 500 is described with regard to the systems and components ofFIG. 1, though it should be appreciated that the method 500 may beimplemented with other systems and components without departing from thescope of the present disclosure. Method 500 may be stored as executableinstructions 172 in the non-transitory memory 162 of the operationconsole 160, for example, and may be executed by one or more of theprocessor 161 and the image processor 150 to perform the actionsdescribed herein below.

Method 500 begins at 505. At 505, method 500 receives a selection of aprotocol. Method 500 specifically receives a selection of an acquisitionprotocol specifying one or more parameters for acquiring an image, suchas tube voltage, tube current, and so on. The acquisition protocol maycomprise an image pasting acquisition protocol, and thus may include adefinition of patient coverage. The acquisition protocol may correspondto a specific view. For example, the acquisition protocol may comprisean APPA acquisition protocol or a lateral acquisition protocol.Furthermore, the acquisition protocol may specify an anatomy to beimaged, such that appropriate acquisition parameters may be selected.Method 500 may receive the selection of the protocol, for example, viathe user interface 163.

At 510, method 500 acquires a plurality of images in at least one view.To that end, method 500 controls the x-ray source 111 and the x-raydetector 134, for example, according to the protocol selected at 505, toacquire a series of radiographic images, while adjusting the relativeposition of the x-ray source 111 and the x-ray detector 134 such thatthe radiographic images depict different but adjacent regions of thesubject 105. In such examples, method 500 may control the x-ray source111 and the x-ray detector 134 to acquire multiple images of the subject105 in a first view (e.g., APPA, lateral, and so on) as well as a secondview different from the first view.

After acquiring the plurality of images, method 500 continues to 515. At515, method 500 inputs the plurality of images to a classificationnetwork, such as the classification network 170. As discussedhereinabove, the classification network 170 is trained to classify ananatomy/view depicted in the acquired images. For example, the anatomymay comprise a head, spine, abdomen, leg, and so on, while the view mayindicate an orientation of the subject with respect to the x-raydetector, expressed in anatomical terms, such as lateral,anterior-posterior (AP), posterior-anterior (PA), and so on. Theanatomy/view classified by the classification network may thus be outputas a combination of the anatomy depicted in an image as well as the viewof the anatomy depicted in the image. For example, the output of theclassification network 170 may comprise an anatomy/view classificationsuch as Spine APPA, Spine Right Lateral, Spine Left Lateral, Leg APPA,and so on. Thus, at 520, method 500 receives predictions of theanatomy/view for each image in the plurality of images. That is, theclassification network 170 outputs a classification of the anatomy/viewfor each image, along with a prediction or a probability regarding howlikely the anatomy/view classification for each image is correct.

Continuing at 525, method 500 determines the anatomy/view for each imagebased on the predictions. As described further herein with regard toFIG. 6, a method for determining the anatomy/view for each image basedon the prediction of the anatomy/view classification output by theclassification network 170 includes evaluating each of the predictionsaccording to one or more conditions to ensure that the images areaccurately classified. Thus, in some examples, method 500 may determinethat the anatomy/view classification for each image predicted by theclassification network 170 is correct. Meanwhile, in other examples,method 500 may adjust an anatomy/view classification of a first imagefrom a first view to a second view, for example, if a second image ispredicted to the same anatomy/view classification as the first imagewhile also having a higher probability of belonging to that anatomy/viewclassification, and if none of the images were classified by theclassification network 170 to the second view at the correspondinganatomy. That is, method 500 may automatically and accurately deduce theappropriate anatomy/view classifications, even in instances of redundantanatomy/view classifications.

After determining the anatomy/view classifications for each image in theplurality of images, method 500 continues to 530. At 530, method 500performs image pasting of the plurality of images based on thedetermined anatomy/views to generate a stitched image for each view. Insome examples, method 500 may process each of the images according tothe respective anatomy/view classifications with post-processingparameters selected according to the anatomy/view classifications.Further, method 500 selects an image pasting protocol for each view toapply to the plurality of images determined to correspond to the view,and then performs image pasting of the plurality of images according tothe image pasting protocol. Thus, images in a lateral view may beautomatically stitched according to a lateral image pasting protocol,while images in an anterior-posterior view may be automatically stitchedaccording to an anterior-posterior image pasting protocol. By using thecorrect image pasting protocol, the registration of the images withinthe view is improved.

At 535, method 500 outputs the stitched image(s). For example, method500 may output the stitched image for each view to the display device155 for display, and additionally may output the stitched image(s) tothe memory 162 and/or a PACS. Method 500 then returns.

FIG. 6 shows a high-level flow chart illustrating an example method 600for determining anatomy/view classifications based on predictions ofanatomy/view classifications for multiple radiographic images accordingto an embodiment. In particular, method 600 may comprise a sub-routineof method 500, for example by comprising the action 525 describedhereinabove. Method 600 specifically relates to determining anappropriate label for a plurality of images acquired in a view, whereinthe label corresponds to the view, and so if multiple views are acquiredduring execution of method 500, method 600 may be executed for eachplurality of images acquired in each view. Method 600 is thereforedescribed with regard to the systems and components of FIG. 1 as well asthe method 500 of FIG. 5, though it should be appreciated that themethod 600 may be implemented with other systems, components, andmethods without departing from the scope of the present disclosure.Method 600 may be stored as executable instructions 172 in thenon-transitory memory 162 of the operation console 160, for example, andmay be executed by one or more of the processor 161 and the imageprocessor 150 to perform the actions described herein below.

Method 600 begins at 605. At 605, method 600 evaluates the predictionsof the anatomy/view for each image in the plurality of images, thepredictions including a predicted label (e.g., Left Lateral, RightLateral, APPA, and so on) and a probability of the predicted label. At610, method 600 determines whether a first condition is satisfied. Inparticular, method 600 determines whether there is a predicted label inthe predictions with a higher occurrence than others. For example, ifthe predictions for two images out of three images are for a LeftLateral view, while the prediction for the third image is for RightLateral view, then the Left Lateral predicted label has a higheroccurrence than the Right Lateral predicted label. In this example, thefirst condition is satisfied. Similarly, if the predictions for allimages include the same predicted label, then the predicted label hasthe highest occurrence, and the first condition is satisfied. Incontrast, if an equal number of predictions are for a first predictedlabel as well as for a second predicted label, then there is nopredicted label with a highest occurrence and the first condition is notsatisfied.

If the first condition is satisfied (“YES”), method 600 continues to612, wherein method 600 classifies the anatomy/view according to thepredicted label with the higher occurrence. That is, all of the imagesin the plurality of images are classified according to the predictedlabel with the higher occurrence. Method 600 then returns.

However, if the first condition is not satisfied (“NO”) at 610, method600 continues to 615. At 615, method 600 determines whether a secondcondition is satisfied. In particular, method 600 determines whetherthere is one predicted label with a higher probability than the otherpredicted labels. For example, if half of the images are predicted to afirst label with a first probability, and the other half of the imagesare predicted to a second label with a second probability, method 600determines whether the first probability is higher than the secondprobability. If one of the probabilities is higher than the other(s),then the second condition is satisfied (“YES”), and method 600 continuesto 617, wherein method 600 determines that label is the predicted labelwith the higher probability, and classifies the anatomy/view for theplurality of images according to the predicted label with the higherprobability. Method 600 then returns.

However, if the second condition is not satisfied (“NO”) at 615, method600 continues to 620. As a particular anatomy/view in the predictions isnot clearly dominant in the predictions, then the anatomy/view is mostlikely APPA. Thus, at 620, method 600 concludes that the labelcorresponds to APPA and classifies the anatomy/view as APPA for allimages in the plurality of images. Method 600 then returns.

As an illustrative example, consider a set of images wherein theprediction for a first image is Right Lateral with a probability of0.553, while the prediction for a second image is Left Lateral with aprobability of 1. According to method 600, the predictions for the setof images does not satisfy the first condition, since there is an equaloccurrence of predicted labels Right Lateral and Left Lateral. However,since the probability for the second image predicted to Left Lateral ishigher than the probability of the first image predicted to RightLateral, the second condition is satisfied and both images areclassified as Left Lateral.

To illustrate the image pasting methods provided herein, FIG. 7 shows aset of example images 700 illustrating the method 500 of FIG. 5according to an embodiment. The set of example images 700 includes aplurality of acquired images 705. The acquired images 705 include afirst set 710 of images and a second set 715 of images, wherein thefirst set 710 includes a first image 711, a second image 712, and athird image 713, while the second set 715 includes a fourth image 715, afifth image 716, and a sixth image 717. As depicted, the first set 710of images is acquired for a first view, in particular a PA view, whilethe second set 715 of images is acquired for a second view differentfrom the first view, in particular a lateral view. The plurality ofacquired images 705 are input to a classification network, such as theclassification network 170, to automatically determine an anatomy/viewclassification for each image in the plurality of acquired images 705.

As depicted, the classification network 170 outputs a set of predictions725 for the acquired images 705, including a first set 730 ofpredictions and a second set 735 of predictions. The first set 730 ofpredictions includes a first prediction 731 for the first image 711, asecond prediction 732 for the second image 712, and a third prediction733 for the third image 713 of the first set 710 of images. The secondset 735 of predictions includes a fourth prediction 736 for the fourthimage 716, a fifth prediction 737 for the fifth image 717, and a sixthprediction 738 for the sixth image 718 of the second set 715.

In particular, the first prediction 731 indicates that the first image711 is a PA view, the second prediction 732 indicates that the secondimage 712 is a PA view, and the third prediction 733 indicates that thethird image 713 is a lateral view. Meanwhile, the fourth prediction 736indicates that the fourth image 716 is a lateral view, the fifthprediction 737 indicates that the fifth image 717 is a lateral view, andthe sixth prediction 738 indicates that the sixth image 718 is a lateralview.

As both the third prediction 733 and the sixth prediction 738 indicatethat the third image 713 and the sixth image 738 correspond to a lateralview with a same anatomy, such that more images are classified to thelateral view than the PA view, the set of predictions 725 is evaluatedas described hereinabove with regard to FIGS. 5 and 6 to determine theappropriate anatomy/view classification for each image. That is, method600 is applied separately to the first set 730 of predictions and thesecond set 735 of predictions. In the first set 730, the occurrence ofthe PA anatomy/view prediction is higher than the occurrence of theLateral anatomy/view prediction, and so the third prediction 733 isre-classified to a PA anatomy/view.

Thus, after applying the decision algorithm of FIG. 6, the classifiedsets 745 are obtained, wherein the first classified set 750 isdefinitively classified to the PA view while the second classified set755 is definitively classified to the lateral view. In particular, thefirst classified set 750 includes a first image 751, a second image 752,and a third image 753 determined as comprising a first view, inparticular the PA view. The first image 751, the second image 752, andthe third image 753 correspond respectively to the first image 711, thesecond image 712, and the third image 713 of the first set 710

The second classified set 755 includes a fourth image 756, a fifth image757, and a sixth image 758 determined as comprising a second view, inparticular the lateral view. The fourth image 756, the fifth image 757,and the sixth image 758 correspond respectively to the fourth image 716,the fifth image 717, and the sixth image 718 of the second set 715 ofimages.

The classified images 745 are then image pasted or stitched to obtainthe pasted images 765. In particular, the first classified set 750 ofimages are stitched according to a PA image pasting protocol to obtainthe first pasted image 770, while the second classified set 755 ofimages are stitched according to a lateral image pasting protocol toobtain the second pasted image 775. By using the appropriate imagepasting protocol to stitch the classified images 745, the accuracy ofthe registration of the classified images 745 is improved, andconsequently the classified images 745 are accurately stitched togenerate the pasted images 765.

Thus, methods and systems are provided for improving the post-processingof one or more acquired images that includes automatically classifyingthe anatomy/view depicted in the one or more acquired images.

In some examples, an x-ray imaging system such as the x-ray imagingsystem 100 may include a camera, such as the camera 120, co-aligned withthe x-ray source 111 to capture camera data of the subject 105. In suchexamples, a classification network such as the classification network170 may be trained to automatically classify the anatomy/view depictedin such camera data. As a result, an appropriate acquisition protocolmay be automatically selected prior to acquiring an x-ray image, withoutmanual input from a user of the x-ray imaging system 100.

As an illustrative example, FIG. 8 shows a high-level flow chartillustrating an example method 800 for anatomy/view classification foracquisition protocol selection according to an embodiment. Inparticular, method 800 relates to classifying an anatomy/view based oncamera data, and then acquiring an x-ray image based on the classifiedanatomy/view. Method 800 is described with regard to the systems andcomponents of FIG. 1, though it should be appreciated that the method800 may be implemented with other systems and components withoutdeparting from the scope of the present disclosure. Method 800 may bestored as executable instructions 172 in the non-transitory memory 162of the operation console 160, for example, and may be executed by one ormore of the processor 161 and the image processor 150 to perform theactions described herein below.

Method 800 begins at 805. At 805, method 800 receives camera data from acamera, such as the camera 120, co-aligned with an x-ray source, such asthe x-ray source 111. The camera data comprises optical ornon-radiographic images of the subject 105, for example. As the camera120 is co-aligned with the x-ray source 111, the camera data thuscaptured depicts the orientation of the subject 105 relative to thex-ray source 111 and the x-ray detector 134. Continuing at 810, method800 inputs the camera data to a classification network, such as theclassification network 170. As discussed hereinabove, the classificationnetwork 170 is trained to classify an anatomy/view depicted in acquiredimages. In addition to classifying the anatomy/view depicted inradiographic images as described hereinabove, the classification network170 may further be trained to classify the anatomy/view depicted incamera data. For example, the anatomy may comprise a head, spine,abdomen, leg, and so on, while the view may indicate an orientation ofthe subject with respect to the x-ray detector, expressed in anatomicalterms, such as lateral, anterior-posterior (AP), posterior-anterior(PA), and so on. The anatomy/view classified by the classificationnetwork may thus be output as a combination of the anatomy depicted inthe camera data as well as the view of the anatomy depicted in thecamera data. For example, the output of the classification network 170may comprise an anatomy/view classification such as Spine APPA, SpineRight Lateral, Spine Left Lateral, Leg APPA, and so on.

At 815, method 800 receives a classification of the anatomy/viewdepicted in the camera data from the classification network. Continuingat 820, method 800 selects a protocol for the x-ray source 111 accordingto the anatomy/view classification. For example, method 800automatically selects one or more acquisition parameters based on theanatomy/view classification. Then, at 825, method 800 controls the x-raysource 111 with the selected protocol to acquire an x-ray orradiographic image. Further, since the anatomy/view is alreadyclassified based on the camera data, it is unnecessary to classify theanatomy/view in the acquired x-ray image with the classification network170. Thus, continuing at 830, method 800 performs post-processing of theacquired image based on the anatomy/view classification as describedhereinabove. At 835, method 800 outputs the post-processed image, forexample, to the display device 155 for display to the user. Method 800may also output the post-processed image to the memory 162 for storageand/or a PACS. Method 800 then returns.

FIG. 9 shows a set of images 900 illustrating anatomy/viewclassification based on camera data according to an embodiment. The setof images 900 includes a camera image 910 acquired via a camera, such ascamera 120, co-aligned with an x-ray source, such as x-ray source 111.The camera image 910 depicts an imaging subject 917 positioned in frontof the detector 915, which may comprise the detector 134 of FIG. 1. Thecamera image 910, also referred to hereinabove as camera data, is inputto the classification network 170 to automatically classify theanatomy/view of the camera image 910. The output 930 of theclassification network 170 includes the camera image 910 as well as alabel or classification 932 of the anatomy/view determined by theclassification network 170. As depicted, the classification 932 outputby the classification network 170 indicates that the view corresponds toan APPA view. It should be appreciated that, while not explicitlydepicted in FIG. 9, the classification 932 may further indicate ananatomy depicted in the camera data 910, such as Spine or Torso. Asdescribed hereinabove, the classification 932 may be used toautomatically select an acquisition protocol for controlling the x-raysource 111 and the x-ray detector 134 to acquire one or moreradiographic images. Furthermore, the classification 932 may also beused to determine view or post-processing parameters for post-processingthe acquired radiographic images. Thus, by including a camera 120 in thex-ray imaging system 100 as well as the classification network 170, theuser may be relieved from selecting the acquisition protocol and/or theview or post-processing protocol.

A technical effect of the disclosure includes a classification of ananatomy and a view depicted in an image. Another technical effect of thedisclosure includes the automatic selection of an acquisition protocoland/or a post-processing protocol according to an anatomy and a view.Yet another technical effect of the disclosure includes the improvedregistration between images in an image pasting examination. Anothertechnical effect of the disclosure includes the accurate processing ofacquired radiographic images according to automatically classifiedanatomies and views depicted in the acquired radiographic images.

In one embodiment, a method comprises controlling an x-ray source and anx-ray detector to acquire an image of a subject, classifying, with atrained neural network, an anatomy/view depicted in the image,performing post-processing of the image based on the anatomy/view, anddisplaying the post-processed image.

In a first example of the method, the method further comprisesdetermining parameters for adjusting one or more of edge details,contrast, and noise level in the image according to the anatomy/view,and performing the post-processing of the image according to thedetermined parameters. In a second example of the method optionallyincluding the first example, the method further comprises controllingthe x-ray source and the x-ray detector to acquire a plurality of imagesof the subject including the image and at least a second image, whereinthe image and the second image depict adjacent and overlapping regionsof the subject. In a third example of the method optionally includingone or more of the first and second examples, the method furthercomprises classifying, with the trained neural network, anatomy/viewsdepicted in the plurality of images, including at least a secondanatomy/view depicted in the second image. In a fourth example of themethod optionally including one or more of the first through thirdexamples, performing the post-processing of the image based on theanatomy/view comprises registering the plurality of images according tothe anatomy/views depicted in the plurality of images, includingregistering the image to the second image according to the anatomy/viewand the second anatomy/view, and stitching the registered plurality ofimages to generate a stitched image, wherein displaying thepost-processed image comprises displaying the stitched image. In a fifthexample of the method optionally including one or more of the firstthrough fourth examples, registering the plurality of images accordingto the anatomy/views depicted in the plurality of images, includingregistering the image to the second image according to the anatomy/viewand the second anatomy/view, comprises determining that theanatomy/views including the anatomy/view and the second anatomy/viewcomprise a same, first anatomy/view, and registering the plurality ofimages according to an image pasting protocol configured for the firstanatomy/view. In a sixth example of the method optionally including oneor more of the first through fifth examples, the method furthercomprises controlling the x-ray source and the x-ray detector to acquirea second plurality of images including at least a third image and afourth image of the subject, wherein the plurality of images includingthe image and at least the second image depict adjacent and overlappingregions of the subject in a first view corresponding to the firstanatomy/view, and wherein the second plurality of images depict adjacentand overlapping regions of the subject in a second view different fromthe first view. In a seventh example of the method optionally includingone or more of the first through sixth examples, the method furthercomprises classifying, with the trained neural network, anatomy/viewsdepicted in the second plurality of images including a thirdanatomy/view depicted in the third image and a fourth anatomy/viewdepicted in the fourth image. In an eighth example of the methodoptionally including one or more of the first through seventh examples,the method further comprises determining that the anatomy/views depictedin the second plurality of images comprise an anatomy/view correspondingto the second view, registering the second plurality of images accordingto an image pasting protocol for the anatomy/view corresponding to thesecond view, stitching the registered second plurality of images togenerate a second stitched image with the second view, and displayingthe second stitched image. In a ninth example of the method optionallyincluding one or more of the first through eighth examples, the methodfurther comprises receiving, from the trained neural network,probabilities for corresponding anatomy/views classified for pluralityof images and the second plurality of images, determining whether thecorresponding anatomy/views are correct based on an occurrence ofanatomy/views and the probabilities, and adjusting a classification ofan anatomy/view for a given image if one of the occurrence ofanatomy/views and the probabilities indicate that the classification ofthe anatomy/view for the given image is incorrect.

In another representation, a method comprises controlling an x-raysource and an x-ray detector to acquire an image of a subject,classifying, with a trained neural network, an anatomy/view depicted inthe image, performing post-processing of the image based on theanatomy/view, and displaying the post-processed image.

In a first example of the method, the method further comprisesdetermining parameters for adjusting one or more of edge details,contrast, and noise level in the image according to the anatomy/view,and performing the post-processing of the image according to thedetermined parameters. In a second example of the method optionallyincluding the first example, the method further comprises controllingthe x-ray source and the x-ray detector to acquire a second image of thesubject, wherein the first image and the second image depict adjacentand overlapping regions of the subject. In a third example of the methodoptionally including one or more of the first and second examples, themethod further comprises classifying, with the trained neural network, asecond anatomy/view depicted in the second image. In a fourth example ofthe method optionally including one or more of the first through thirdexamples, performing the post-processing of the image based on theanatomy/view comprises registering the image to the second imageaccording to the anatomy/view and the second anatomy/view, and stitchingthe image to the second image to generate a stitched image, anddisplaying the post-processed image comprises displaying the stitchedimage. In a fifth example of the method optionally including one or moreof the first through fourth examples, the method further comprisesadjusting contrast and performing noise reduction for the image and thesecond image according to the anatomy/view and the second anatomy/view,respectively, prior to registering the image to the second image. In asixth example of the method optionally including one or more of thefirst through fifth examples, the method further comprises controllingthe x-ray source and the x-ray detector to acquire a third image and afourth image of the subject, wherein the first image and the secondimage depict adjacent and overlapping regions of the subject in a firstview, and wherein the third image and the fourth image depict adjacentand overlapping regions of the subject in a second view different fromthe first view. In a seventh example of the method optionally includingone or more of the first through sixth examples, the method furthercomprises classifying, with the trained neural network, a thirdanatomy/view depicted in the third image and a fourth anatomy/viewdepicted in the fourth image. In an eighth example of the methodoptionally including one or more of the first through seventh examples,the method further comprises registering the third image to the fourthimage according to the third anatomy/view and the fourth anatomy/view,stitching the third image to the fourth image to generate a secondstitched image with the second view, and displaying the second stitchedimage. In a ninth example of the method optionally including one or moreof the first through eighth examples, the method further comprisesreceiving, from the trained neural network, probabilities forcorresponding anatomy/views classified for the image, the second image,the third image, and the fourth image, and determining whether thecorresponding anatomy/views are correct based on the probabilities priorto second probability, respectively.

In another embodiment, a method comprises acquiring one or more imagesof a subject, determining, from the one or more images of the subjectwith a classification network, an orientation and a position of thesubject relative to an x-ray source and an x-ray detector, processingone or more x-ray images of the subject acquired with the x-ray sourceand the x-ray detector based on the orientation and the position of thesubject, and displaying the one or more processed x-ray images.

In a first example of the method, acquiring the one or more images ofthe subject comprises acquiring, with an optical camera co-aligned withthe x-ray source, the one or more images, and further comprisingselecting one or more acquisition parameters based on the orientationand the position, and controlling the x-ray source and the x-raydetector according to the one or more acquisition parameters to acquirethe one or more x-ray images. In a second example of the methodoptionally including the first example, the one or more images comprisesthe one or more x-ray images, and processing the one or more x-rayimages comprises adjusting an appearance of the one or more x-ray imagesaccording to a view protocol selected based on the orientation and theposition of the subject. In a third example of the method optionallyincluding one or more of the first and second examples, the one or morex-ray images comprises a first plurality of x-ray images of the subjectin a first orientation, and a second plurality of x-ray images of thesubject in a second orientation, wherein determining the orientationwith the classification network comprises determining the firstorientation for the first plurality of x-ray images and the secondorientation of the second plurality of x-ray images. In a fourth exampleof the method optionally including one or more of the first throughthird examples, processing the one or more x-ray images based on theorientation and position of the subject comprises registering andstitching the first plurality of x-ray images based on the firstorientation into a first stitched image with a first field-of-viewlarger than each of the first plurality of x-ray images, and registeringand stitching the second plurality of x-ray images based on the secondorientation in to a second stitched image with a second field-of-viewlarger than each of the second plurality of x-ray images.

In yet another embodiment, an x-ray imaging system comprises an x-raysource for generating x-rays, an x-ray detector configured to detect thex-rays, a display device, and a processor configured with instructionsin non-transitory memory that when executed cause the processor to:control the x-ray source and the x-ray detector to acquire an image of asubject; classify, with a trained neural network, an anatomy/viewdepicted in the image; perform post-processing of the image based on theanatomy/view; and display, via the display device, the post-processedimage.

In a first example of the system, the processor is further configuredwith instructions in the non-transitory memory that when executed causethe processor to determine parameters for adjusting one or more of edgedetails, contrast, and noise level in the image according to theanatomy/view, and perform the post-processing of the image according tothe determined parameters. In a second example of the system optionallyincluding the first example, controlling the x-ray source and the x-raydetector to acquire the image comprises controlling the x-ray source andthe x-ray detector to acquire a plurality of images of the subject, andclassifying the anatomy/view depicted in the image comprisesclassifying, with the trained neural network, an anatomy/view depictedin each image of the plurality of images. In a third example of thesystem optionally including one or more of the first and secondexamples, the processor is further configured with instructions in thenon-transitory memory that when executed cause the processor todetermine a first plurality of images in the plurality of imagescorresponding to a first view, determine a second plurality of images inthe plurality of images corresponding to a second view, register andstitch the first plurality of images into a first stitched imageaccording to the first view, and register and stitch the secondplurality of images into a second stitched image according to the secondview. In a fourth example of the system optionally including one or moreof the first through third examples, the system further comprises anoptical camera positioned adjacent to the x-ray source and co-alignedwith the x-ray source, wherein the processor is further configured withinstructions in the non-transitory memory that when executed cause theprocessor to: acquire camera data via the optical camera; classify, withthe trained neural network, an anatomy/view of the subject depicted inthe camera data; select one or more acquisition parameters based on theanatomy/view of the subject depicted in the camera data; and control thex-ray source and the x-ray detector with the one or more acquisitionparameters to acquire the image of the subject.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralof said elements or steps, unless such exclusion is explicitly stated.Furthermore, references to “one embodiment” of the present invention arenot intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features. Moreover, unlessexplicitly stated to the contrary, embodiments “comprising,”“including,” or “having” an element or a plurality of elements having aparticular property may include additional such elements not having thatproperty. The terms “including” and “in which” are used as theplain-language equivalents of the respective terms “comprising” and“wherein.” Moreover, the terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements or a particular positional order on their objects.

This written description uses examples to disclose the invention,including the best mode, and also to enable a person of ordinary skillin the relevant art to practice the invention, including making andusing any devices or systems and performing any incorporated methods.The patentable scope of the invention is defined by the claims, and mayinclude other examples that occur to those of ordinary skill in the art.Such other examples are intended to be within the scope of the claims ifthey have structural elements that do not differ from the literallanguage of the claims, or if they include equivalent structuralelements with insubstantial differences from the literal languages ofthe claims.

1. A method, comprising: controlling an x-ray source and an x-raydetector to acquire an image of a subject; classifying, with a trainedneural network, an anatomy/view depicted in the image; performingpost-processing of the image based on the anatomy/view; and displayingthe post-processed image.
 2. The method of claim 1, further comprisingdetermining parameters for adjusting one or more of edge details,contrast, and noise level in the image according to the anatomy/view,and performing the post-processing of the image according to thedetermined parameters.
 3. The method of claim 1, further comprisingcontrolling the x-ray source and the x-ray detector to acquire aplurality of images of the subject including the image and at least asecond image, wherein the image and the second image depict adjacent andoverlapping regions of the subject.
 4. The method of claim 3, furthercomprising classifying, with the trained neural network, anatomy/viewsdepicted in the plurality of images, including at least a secondanatomy/view depicted in the second image.
 5. The method of claim 4,wherein performing the post-processing of the image based on theanatomy/view comprises registering the plurality of images according tothe anatomy/views depicted in the plurality of images, includingregistering the image to the second image according to the anatomy/viewand the second anatomy/view, and stitching the registered plurality ofimages to generate a stitched image, and wherein displaying thepost-processed image comprises displaying the stitched image.
 6. Themethod of claim 5, wherein registering the plurality of images accordingto the anatomy/views depicted in the plurality of images, includingregistering the image to the second image according to the anatomy/viewand the second anatomy/view, comprises determining that theanatomy/views including the anatomy/view and the second anatomy/viewcomprise a same, first anatomy/view, and registering the plurality ofimages according to an image pasting protocol configured for the firstanatomy/view.
 7. The method of claim 6, further comprising controllingthe x-ray source and the x-ray detector to acquire a second plurality ofimages including at least a third image and a fourth image of thesubject, wherein the plurality of images including the image and atleast the second image depict adjacent and overlapping regions of thesubject in a first view corresponding to the first anatomy/view, andwherein the second plurality of images depict adjacent and overlappingregions of the subject in a second view different from the first view.8. The method of claim 7, further comprising classifying, with thetrained neural network, anatomy/views depicted in the second pluralityof images including a third anatomy/view depicted in the third image anda fourth anatomy/view depicted in the fourth image.
 9. The method ofclaim 8, further comprising determining that the anatomy/views depictedin the second plurality of images comprise an anatomy/view correspondingto the second view, registering the second plurality of images accordingto an image pasting protocol for the anatomy/view corresponding to thesecond view, stitching the registered second plurality of images togenerate a second stitched image with the second view, and displayingthe second stitched image.
 10. The method of claim 9, further comprisingreceiving, from the trained neural network, probabilities forcorresponding anatomy/views classified for plurality of images and thesecond plurality of images, determining whether the correspondinganatomy/views are correct based on an occurrence of anatomy/views andthe probabilities, and adjusting a classification of an anatomy/view fora given image if one of the occurrence of anatomy/views and theprobabilities indicate that the classification of the anatomy/view forthe given image is incorrect.
 11. A method, comprising: acquiring one ormore images of a subject; determining, from the one or more images ofthe subject with a classification network, an orientation and a positionof the subject relative to an x-ray source and an x-ray detector;processing one or more x-ray images of the subject acquired with thex-ray source and the x-ray detector based on the orientation and theposition of the subject; and displaying the one or more processed x-rayimages.
 12. The method of claim 11, wherein acquiring the one or moreimages of the subject comprises acquiring, with an optical cameraco-aligned with the x-ray source, the one or more images, and furthercomprising selecting one or more acquisition parameters based on theorientation and the position, and controlling the x-ray source and thex-ray detector according to the one or more acquisition parameters toacquire the one or more x-ray images.
 13. The method of claim 11,wherein the one or more images comprises the one or more x-ray images,and wherein processing the one or more x-ray images comprises adjustingan appearance of the one or more x-ray images according to a viewprotocol selected based on the orientation and the position of thesubject.
 14. The method of claim 13, wherein the one or more x-rayimages comprises a first plurality of x-ray images of the subject in afirst orientation, and a second plurality of x-ray images of the subjectin a second orientation, wherein determining the orientation with theclassification network comprises determining the first orientation forthe first plurality of x-ray images and the second orientation of thesecond plurality of x-ray images.
 15. The method of claim 14, whereinprocessing the one or more x-ray images based on the orientation andposition of the subject comprises registering and stitching the firstplurality of x-ray images based on the first orientation into a firststitched image with a first field-of-view larger than each of the firstplurality of x-ray images, and registering and stitching the secondplurality of x-ray images based on the second orientation in to a secondstitched image with a second field-of-view larger than each of thesecond plurality of x-ray images.
 16. An x-ray imaging system,comprising: an x-ray source for generating x-rays; an x-ray detectorconfigured to detect the x-rays; a display device; and a processorconfigured with instructions in non-transitory memory that when executedcause the processor to: control the x-ray source and the x-ray detectorto acquire an image of a subject; classify, with a trained neuralnetwork, an anatomy/view depicted in the image; perform post-processingof the image based on the anatomy/view; and display, via the displaydevice, the post-processed image.
 17. The system of claim 16, whereinthe processor is further configured with instructions in thenon-transitory memory that when executed cause the processor todetermine parameters for adjusting one or more of edge details,contrast, and noise level in the image according to the anatomy/view,and perform the post-processing of the image according to the determinedparameters.
 18. The system of claim 16, wherein controlling the x-raysource and the x-ray detector to acquire the image comprises controllingthe x-ray source and the x-ray detector to acquire a plurality of imagesof the subject, and wherein classifying the anatomy/view depicted in theimage comprises classifying, with the trained neural network, ananatomy/view depicted in each image of the plurality of images.
 19. Thesystem of claim 18, wherein the processor is further configured withinstructions in the non-transitory memory that when executed cause theprocessor to determine a first plurality of images in the plurality ofimages corresponding to a first view, determine a second plurality ofimages in the plurality of images corresponding to a second view,register and stitch the first plurality of images into a first stitchedimage according to the first view, and register and stitch the secondplurality of images into a second stitched image according to the secondview.
 20. The system of claim 16, further comprising an optical camerapositioned adjacent to the x-ray source and co-aligned with the x-raysource, wherein the processor is further configured with instructions inthe non-transitory memory that when executed cause the processor to:acquire camera data via the optical camera; classify, with the trainedneural network, an anatomy/view of the subject depicted in the cameradata; select one or more acquisition parameters based on theanatomy/view of the subject depicted in the camera data; and control thex-ray source and the x-ray detector with the one or more acquisitionparameters to acquire the image of the subject.